SciML/DataDrivenDiffEq.jl

Better inform the parameter set and the statistical measures

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#106 opened on May 21, 2020

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Description

In the print equations and parameters, three improvements are needed:

  1. The parameter set should be shrunk to eliminate all duplicates (ill-conditioning of Hessian), as is also the case in Basis (ill-conditioning of regression). In the Lorenz example, the initial guess of 7 parameters can be shrunk to the final three parameters by observing that the absolute values of P1 and P2 are identical to double or even quadruple precision; P4, P5 and P7 are also identical. This leaves P1, P3 and P4 as the true parameters discovered by the reconstruction.

  2. The parameters should be presented with their standard errors to nail the point that the reconstruction method is extremely robust. Also print them with just 2 or 3 significant decimals.

  3. The AIC, AICC and BIC statistics should be printed out with the corresponding pass/fail statistical values or send the user to the docstring.

Contributor guide

Research direction
Understand the current parameter printing logic in the library, study how duplicates arise from ill conditioned Hessians, implement deduplication, add standard error computation and printing, and include AIC, AICC, BIC statistics with pass/fail indicators.
Tech stack
julia
Domain
backend
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
3-5 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
JuliaDynamical SystemsStatistics
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
70